We evaluate six state-of-the-art multi-view methods, including three multi-view representation learning models and three multi-view clustering models.
We also evaluate a naive baseline for multi-view methods that simply concatenates the ResNet-50 features and the Doc2Vec features before applying K-means and DEC as in the single-view case. (1) VSE [1]}: Unifying Visual-Semantic Embeddings unifies joint image-text embedding models by minimizing pairwise ranking loss. K-means is implemented to acquire the cluster centroids. (2) DCCA [2]}: Deep Canonical Correlation Analysis learns complex nonlinear transformations of two views of data by maximizing the regularized total correlation. The cluster assignments are obtained with K-means on the joint representations. (3) CCAL-\(L_{rank}\): This method learns a joint representation by maximizing Canonical Correlation with a pairwise ranking loss. K-means is applied to the learned embeddings. (4) DMF-MVC [3]}: Multi-View Clustering via Deep Matrix Factorization is a deep matrix factorization framework to learn the semantic information of all views in a layer-wise fashion. (5) BMVC [4]}: Binary Multi-view Clustering is a joint learning framework simultaneously compressing inputs into collaborative binary representations and clustering the collaborative representations using a binary matrix factorization model. (6) MLRSSC [5]}: Multi-view Low-rank Sparse Subspace Clustering is a multi-view spectral clustering framework that learns a joint subspace representation by building affinity matrix among all views with the constraints of low-rank and sparsity.
